Manager, ML Engineering

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What The Market Looks Like
We've tracked 76 management-level ML engineering postings in the US over the last six months, concentrated in California, Washington, and New York across technology, financial services, and professional services sectors. Compensation for this cohort ranges from $200k to $340k annually. The strongest candidates bring hands-on experience shipping production ML systems alongside proven ability to recruit, mentor, and scale engineering teams—they're equally comfortable diving into model validation as they are unblocking reports and setting team direction.
Job Responsibilities
Lead and grow an ML engineering team, owning hiring, mentoring, and career development while maintaining hands-on technical credibility
Drive the design and deployment of ML systems from research to production, including model training pipelines, inference infrastructure, and monitoring
Partner with data science, product, and platform engineering teams to translate business requirements into technical roadmaps and prioritized deliverables
Own code quality, testing standards, and technical debt management across the team's scope
Establish best practices for model governance, reproducibility, and performance tracking in production environments
Advocate for team needs and budget, communicating technical constraints and opportunities to leadership in business terms
Contribute to company-wide ML strategy and engineering standards, not just team execution
Candidate Requirements
5+ years of hands-on ML engineering experience, including at least 2 years shipping production ML systems (models, pipelines, inference services, or similar)
2+ years in a people leadership or technical mentorship role, demonstrating ability to recruit and develop engineers
Fluency in ML tooling and infrastructure—model training frameworks, experiment tracking, feature stores, deployment platforms, and monitoring—relevant to your domain
Demonstrated ability to balance technical depth with business priorities, translating between engineering and non-technical stakeholders
Experience building and maintaining production systems at scale; comfort with data pipelines, testing, and observability

Work arrangement
Yes